{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 下载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential, layers\n",
    "\n",
    "ds, info = tfds.load('imdb_reviews/subwords8k', with_info=True,\n",
    "                          as_supervised=True)\n",
    "train_ds, test_ds = ds['train'], ds['test']\n",
    "\n",
    "BUFFER_SIZE, BATCH_SIZE = 10000, 64\n",
    "train_ds = train_ds.shuffle(BUFFER_SIZE)\n",
    "train_ds = train_ds.padded_batch(BATCH_SIZE, train_ds.output_shapes)\n",
    "test_ds = test_ds.padded_batch(BATCH_SIZE, test_ds.output_shapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 文本预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "词汇个数: 8185\n",
      "向量化文本: [6351, 7961, 7, 703, 3108, 999, 999, 7975, 2449]\n",
      "6351 --> welcome\n",
      "7961 -->  \n",
      "7 --> to \n",
      "703 --> ge\n",
      "3108 --> ek\n",
      "999 --> tu\n",
      "999 --> tu\n",
      "7975 --> .\n",
      "2449 --> com\n"
     ]
    }
   ],
   "source": [
    "# geektutu.com\n",
    "tokenizer = info.features['text'].encoder\n",
    "print ('词汇个数:', tokenizer.vocab_size)\n",
    "\n",
    "sample_str = 'welcome to geektutu.com'\n",
    "tokenized_str = tokenizer.encode(sample_str)\n",
    "print ('向量化文本:', tokenized_str)\n",
    "\n",
    "for ts in tokenized_str:\n",
    "    print (ts, '-->', tokenizer.decode([ts]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 搭建 RNN 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "     45/Unknown - 132s 3s/step - loss: 0.6927 - accuracy: 0.5083"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    layers.Embedding(tokenizer.vocab_size, 64),\n",
    "    layers.Bidirectional(layers.LSTM(64)),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "history1 = model.fit(train_ds, epochs=10, validation_data=test_ds)\n",
    "loss, acc = model.evaluate(test_ds)\n",
    "print('准确率:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# geektutu.com\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams['font.size'] = 20\n",
    "\n",
    "def plot_graphs(history, name):\n",
    "    plt.plot(history.history[name])\n",
    "    plt.plot(history.history['val_'+ name])\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(name)\n",
    "    plt.legend([name, '验证集 - ' + name])\n",
    "    plt.show()\n",
    "\n",
    "plot_graphs(history1, 'accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 添加更多 LSTM 层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "391/391 [==============================] - 1811s 5s/step - loss: 0.5664 - accuracy: 0.6991 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/10\n",
      "391/391 [==============================] - 1513s 4s/step - loss: 0.3780 - accuracy: 0.8415 - val_loss: 0.4220 - val_accuracy: 0.8242\n",
      "Epoch 3/10\n",
      "391/391 [==============================] - 1461s 4s/step - loss: 0.3037 - accuracy: 0.8775 - val_loss: 0.4419 - val_accuracy: 0.8112\n",
      "Epoch 4/10\n",
      "391/391 [==============================] - 1450s 4s/step - loss: 0.2260 - accuracy: 0.9142 - val_loss: 0.4181 - val_accuracy: 0.8461\n",
      "Epoch 5/10\n",
      "391/391 [==============================] - 1434s 4s/step - loss: 0.1900 - accuracy: 0.9297 - val_loss: 0.4600 - val_accuracy: 0.8032\n",
      "Epoch 6/10\n",
      "391/391 [==============================] - 1436s 4s/step - loss: 0.3483 - accuracy: 0.8509 - val_loss: 0.4899 - val_accuracy: 0.7797\n",
      "Epoch 7/10\n",
      "391/391 [==============================] - 1425s 4s/step - loss: 0.3232 - accuracy: 0.8670 - val_loss: 0.4060 - val_accuracy: 0.8207\n",
      "Epoch 8/10\n",
      "391/391 [==============================] - 1437s 4s/step - loss: 0.2113 - accuracy: 0.9182 - val_loss: 0.4142 - val_accuracy: 0.8446\n",
      "Epoch 9/10\n",
      "391/391 [==============================] - 1445s 4s/step - loss: 0.1486 - accuracy: 0.9465 - val_loss: 0.4460 - val_accuracy: 0.8392\n",
      "Epoch 10/10\n",
      "391/391 [==============================] - 1431s 4s/step - loss: 0.0923 - accuracy: 0.9696 - val_loss: 0.5298 - val_accuracy: 0.8310\n",
      "    391/Unknown - 435s 1s/step - loss: 0.5298 - accuracy: 0.8310准确率: 0.83096\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    layers.Embedding(tokenizer.vocab_size, 64),\n",
    "    layers.Bidirectional(layers.LSTM(64, return_sequences=True)),\n",
    "    layers.Bidirectional(layers.LSTM(32)),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "history = model.fit(train_ds, epochs=10, validation_data=test_ds)\n",
    "loss, acc = model.evaluate(test_ds)\n",
    "print('准确率:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graphs(history, 'accuracy')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
